The problem of flakiness occurs when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the software engineering research community has been working toward defining approaches for detecting and addressing test flakiness, but most of these approaches suffer from scalability issues. Recently, this limitation has been targeted through machine learning solutions that could predict flaky tests using various features, both static and dynamic. Unfortunately, the proposed solutions involve features that could be costly to compute. In this paper, I perform a step forward and predict test flakiness \emph{only using statically computable metrics.} I conducted an experiment on 18 projects coming from the \textsc{FlakeFlagger} dataset. First, I statistically assess the differences between flaky and non-flaky tests in terms of 25 static metrics in an individual and combined way. Then, I experimented with a machine learning approach that predicts flakiness based on the previously evaluated factors. The results show that static features can be used to characterize flaky tests: this is especially true for metrics and smells connected to source code complexity. In addition, this new static approach has performance comparable to the machine learning models already in the literature in terms of F-Measure.